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Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

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  • Laura I Luna

    (National University of Córdoba, Argentina)

Abstract

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

Suggested Citation

  • Laura I Luna, 2022. "Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina," Tourism Economics, , vol. 28(2), pages 559-579, March.
    • Handle: RePEc:sae:toueco:v:28:y:2022:i:2:p:559-579
    DOI: 10.1177/1354816620987681
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    References listed on IDEAS

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    Cited by:

    1. Natalia Porto & Vicente Ramos, 2022. "Developments in the field of tourism economics: The seventh IATE (International Association for Tourism Economics) conference," Tourism Economics, , vol. 28(2), pages 473-475, March.

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